{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bcef7ecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8eed3a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82175d38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "dtype: int64 <class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(s1,type(s1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f02e6c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "k1    v1\n",
       "k2    v2\n",
       "k3    v3\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series({'k1':'v1','k2':'v2','k3':'v3'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "019e3289",
   "metadata": {},
   "outputs": [],
   "source": [
    "s2 = pd.Series(['a','b','c','d'],index=['10','20','30','40'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e837440c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    a\n",
       "20    b\n",
       "30    c\n",
       "40    d\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d08c1fe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2['20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "51384a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "s3 = pd.Series([10,20,30,40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f92b4472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aafa2cc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b2f2290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30, 40], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3082789d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed593e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({\"name\":[\"张三\",\"李四\",\"王二\"],\"age\":[20,21,22],\"gender\":[\"男\",\"女\",\"男\"]})\n",
    "#传入一个字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61ec18a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>20</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王二</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  age gender\n",
       "0   张三   20      男\n",
       "1   李四   21      女\n",
       "2   王二   22      男"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7bd1713a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af2c9b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>20</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王二</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  age gender\n",
       "0   张三   20      男\n",
       "1   李四   21      女\n",
       "2   王二   22      男"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9380cd10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  name  age gender\n",
      "0   张三   20      男\n",
      "1   李四   21      女\n",
      "2   王二   22      男\n"
     ]
    }
   ],
   "source": [
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8915ee7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    张三\n",
       "1    李四\n",
       "2    王二\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['name']\n",
    "#在DataFrame中，索引默认传列名\n",
    "#Series类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cbaef82f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1[['name','age']]\n",
    "#两列的话，以列表为索引传出\n",
    "#DataFrame类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a1600e0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "stu = pd.read_csv(\"./data/students.txt\",names=['id','name','age','gender','clazz'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f4c2dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1500100006</td>\n",
       "      <td>边昂雄</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科二班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1500100007</td>\n",
       "      <td>尚孤风</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1500100008</td>\n",
       "      <td>符半双</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1500100009</td>\n",
       "      <td>沈德昌</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科一班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1500100010</td>\n",
       "      <td>羿彦昌</td>\n",
       "      <td>23</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1500100011</td>\n",
       "      <td>宰运华</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1500100012</td>\n",
       "      <td>梁易槐</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>理科一班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1500100013</td>\n",
       "      <td>逯君昊</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科二班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1500100014</td>\n",
       "      <td>羿旭炎</td>\n",
       "      <td>23</td>\n",
       "      <td>男</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1500100015</td>\n",
       "      <td>宦怀绿</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>理科一班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1500100016</td>\n",
       "      <td>潘访烟</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科一班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1500100017</td>\n",
       "      <td>高芷天</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1500100018</td>\n",
       "      <td>骆怜雪</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1500100019</td>\n",
       "      <td>娄曦之</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1500100020</td>\n",
       "      <td>杭振凯</td>\n",
       "      <td>23</td>\n",
       "      <td>男</td>\n",
       "      <td>理科四班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id name  age gender clazz\n",
       "0   1500100001  施笑槐   22      女  文科六班\n",
       "1   1500100002  吕金鹏   24      男  文科六班\n",
       "2   1500100003  单乐蕊   22      女  理科六班\n",
       "3   1500100004  葛德曜   24      男  理科三班\n",
       "4   1500100005  宣谷芹   22      女  理科五班\n",
       "5   1500100006  边昂雄   21      男  理科二班\n",
       "6   1500100007  尚孤风   23      女  文科六班\n",
       "7   1500100008  符半双   22      女  理科六班\n",
       "8   1500100009  沈德昌   21      男  理科一班\n",
       "9   1500100010  羿彦昌   23      男  理科六班\n",
       "10  1500100011  宰运华   21      男  理科三班\n",
       "11  1500100012  梁易槐   21      女  理科一班\n",
       "12  1500100013  逯君昊   24      男  文科二班\n",
       "13  1500100014  羿旭炎   23      男  理科五班\n",
       "14  1500100015  宦怀绿   21      女  理科一班\n",
       "15  1500100016  潘访烟   23      女  文科一班\n",
       "16  1500100017  高芷天   21      女  理科五班\n",
       "17  1500100018  骆怜雪   21      女  文科六班\n",
       "18  1500100019  娄曦之   24      男  理科三班\n",
       "19  1500100020  杭振凯   23      男  理科四班"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "622a15f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1500100996</td>\n",
       "      <td>厉运凡</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1500100997</td>\n",
       "      <td>陶敬曦</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1500100998</td>\n",
       "      <td>容昆宇</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1500100999</td>\n",
       "      <td>钟绮晴</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1500101000</td>\n",
       "      <td>符瑞渊</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id name  age\n",
       "0    1500100001  施笑槐   22\n",
       "1    1500100002  吕金鹏   24\n",
       "2    1500100003  单乐蕊   22\n",
       "3    1500100004  葛德曜   24\n",
       "4    1500100005  宣谷芹   22\n",
       "..          ...  ...  ...\n",
       "995  1500100996  厉运凡   24\n",
       "996  1500100997  陶敬曦   21\n",
       "997  1500100998  容昆宇   22\n",
       "998  1500100999  钟绮晴   23\n",
       "999  1500101000  符瑞渊   23\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu[['id','name','age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bc9465c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      施笑槐\n",
       "1      吕金鹏\n",
       "2      单乐蕊\n",
       "3      葛德曜\n",
       "4      宣谷芹\n",
       "      ... \n",
       "995    厉运凡\n",
       "996    陶敬曦\n",
       "997    容昆宇\n",
       "998    钟绮晴\n",
       "999    符瑞渊\n",
       "Name: name, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bc108be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1500100013</td>\n",
       "      <td>逯君昊</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科二班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1500100019</td>\n",
       "      <td>娄曦之</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1500100028</td>\n",
       "      <td>幸浩邈</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>1500100989</td>\n",
       "      <td>柏盼香</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>1500100992</td>\n",
       "      <td>莫运盛</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>1500100994</td>\n",
       "      <td>相凌青</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>理科四班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>1500100995</td>\n",
       "      <td>寿芷卉</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1500100996</td>\n",
       "      <td>厉运凡</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科三班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>260 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id name  age gender clazz\n",
       "1    1500100002  吕金鹏   24      男  文科六班\n",
       "3    1500100004  葛德曜   24      男  理科三班\n",
       "12   1500100013  逯君昊   24      男  文科二班\n",
       "18   1500100019  娄曦之   24      男  理科三班\n",
       "27   1500100028  幸浩邈   24      男  理科五班\n",
       "..          ...  ...  ...    ...   ...\n",
       "988  1500100989  柏盼香   24      女  理科六班\n",
       "991  1500100992  莫运盛   24      男  理科六班\n",
       "993  1500100994  相凌青   24      女  理科四班\n",
       "994  1500100995  寿芷卉   24      女  理科五班\n",
       "995  1500100996  厉运凡   24      男  文科三班\n",
       "\n",
       "[260 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu['age']>23\n",
    "#布尔索引\n",
    "stu[stu['age']>23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6e665cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "clazz_num = stu.groupby('clazz')['id'].count()\n",
    "#求各班级人数\n",
    "#对班级进行分组，按id求count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "855d7980",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>clazz</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文科五班</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文科六班</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clazz   id\n",
       "0  文科一班   72\n",
       "1  文科三班   94\n",
       "2  文科二班   87\n",
       "3  文科五班   84\n",
       "4  文科六班  104"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_num=clazz_num.reset_index()\n",
    "#reset_index() 重新分配索引\n",
    "clazz_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "53123b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#对id重新命名\n",
    "clazz_num.rename(columns={\"id\":\"cnt\"},inplace=True)\n",
    "#inplace=True 对自身做修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "75f5fdf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <td>文科一班</td>\n",
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       "      <th>2</th>\n",
       "      <td>文科二班</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文科五班</td>\n",
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       "    <tr>\n",
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       "      <td>文科六班</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>文科四班</td>\n",
       "      <td>81</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>理科一班</td>\n",
       "      <td>78</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>理科三班</td>\n",
       "      <td>68</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>理科二班</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>理科五班</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>理科六班</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>理科四班</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clazz  cnt\n",
       "0   文科一班   72\n",
       "1   文科三班   94\n",
       "2   文科二班   87\n",
       "3   文科五班   84\n",
       "4   文科六班  104\n",
       "5   文科四班   81\n",
       "6   理科一班   78\n",
       "7   理科三班   68\n",
       "8   理科二班   79\n",
       "9   理科五班   70\n",
       "10  理科六班   92\n",
       "11  理科四班   91"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a5e0f305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>91</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>87</td>\n",
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       "      <th>3</th>\n",
       "      <td>文科五班</td>\n",
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       "      <td>文科四班</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>理科二班</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>理科一班</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>理科五班</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>理科三班</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clazz  cnt\n",
       "4   文科六班  104\n",
       "1   文科三班   94\n",
       "10  理科六班   92\n",
       "11  理科四班   91\n",
       "2   文科二班   87\n",
       "3   文科五班   84\n",
       "5   文科四班   81\n",
       "8   理科二班   79\n",
       "6   理科一班   78\n",
       "0   文科一班   72\n",
       "9   理科五班   70\n",
       "7   理科三班   68"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_num.sort_values('cnt',ascending=False)\n",
    "#clazz_num.sort_values('cnt')默认升序\n",
    "#ascending=False 降序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5d24f3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>clazz</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文科六班</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>理科六班</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>理科四班</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clazz  cnt\n",
       "4   文科六班  104\n",
       "1   文科三班   94\n",
       "10  理科六班   92\n",
       "11  理科四班   91\n",
       "2   文科二班   87"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_num.sort_values('cnt',ascending=False).head(5)\n",
    "#clazz_num.sort_values('cnt',ascending=False)[0:5]\n",
    "#取出班级人数前五的班级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b8addeb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clazz</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文科六班</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>理科六班</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>理科四班</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clazz  cnt\n",
       "4   文科六班  104\n",
       "1   文科三班   94\n",
       "10  理科六班   92\n",
       "11  理科四班   91\n",
       "2   文科二班   87"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_num.sort_values('cnt',ascending=False)[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1b9395f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clazz</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文科五班</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  clazz  cnt\n",
       "0  文科一班   72\n",
       "1  文科三班   94\n",
       "2  文科二班   87\n",
       "3  文科五班   84"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_num.loc[0:3]\n",
    "#取出索引为0，1，2，3的班级"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cc0b4849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz\n",
       "0  1500100001  施笑槐   22      女  文科六班\n",
       "1  1500100002  吕金鹏   24      男  文科六班\n",
       "2  1500100003  单乐蕊   22      女  理科六班\n",
       "3  1500100004  葛德曜   24      男  理科三班\n",
       "4  1500100005  宣谷芹   22      女  理科五班"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "875cb192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      1000 non-null   int64 \n",
      " 1   name    1000 non-null   object\n",
      " 2   age     1000 non-null   int64 \n",
      " 3   gender  1000 non-null   object\n",
      " 4   clazz   1000 non-null   object\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 39.2+ KB\n"
     ]
    }
   ],
   "source": [
    "stu.info()\n",
    "#列出表的列属性的详细信息,具体类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "31983a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.021497</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id       age\n",
       "id   1.000000  0.021497\n",
       "age  0.021497  1.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.corr()\n",
    "#计算列与列之间的相关系数，返回相关系数矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9f657cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.500101e+09</td>\n",
       "      <td>22.521000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.888194e+02</td>\n",
       "      <td>1.113013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.500100e+09</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500100e+09</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.500101e+09</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.500101e+09</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.500101e+09</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          age\n",
       "count  1.000000e+03  1000.000000\n",
       "mean   1.500101e+09    22.521000\n",
       "std    2.888194e+02     1.113013\n",
       "min    1.500100e+09    21.000000\n",
       "25%    1.500100e+09    22.000000\n",
       "50%    1.500101e+09    22.000000\n",
       "75%    1.500101e+09    24.000000\n",
       "max    1.500101e+09    24.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.describe()\n",
    "#对数值型的列求标准差、最小最大值、count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f19c38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = pd.read_csv(\"./data/score.txt\",names=['student_id','subject_id','score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ea3137d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>subject_id</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000001</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000002</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000003</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000004</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>1000005</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   student_id  subject_id  score\n",
       "0  1500100001     1000001     98\n",
       "1  1500100001     1000002      5\n",
       "2  1500100001     1000003    137\n",
       "3  1500100001     1000004     29\n",
       "4  1500100001     1000005     85"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c069c427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>name</th>\n",
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       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz\n",
       "0  1500100001  施笑槐   22      女  文科六班\n",
       "1  1500100002  吕金鹏   24      男  文科六班\n",
       "2  1500100003  单乐蕊   22      女  理科六班\n",
       "3  1500100004  葛德曜   24      男  理科三班\n",
       "4  1500100005  宣谷芹   22      女  理科五班"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "007ea64e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#求班级总分前三名\n",
    "#TopN\n",
    "score_sum = score.groupby('student_id')['score'].sum().reset_index().rename(columns={'score':'score_sum'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b79a213c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>student_id</th>\n",
       "      <th>score_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1500100996</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1500100997</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1500100998</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1500100999</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1500101000</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     student_id  score_sum\n",
       "0    1500100001        406\n",
       "1    1500100002        440\n",
       "2    1500100003        359\n",
       "3    1500100004        421\n",
       "4    1500100005        395\n",
       "..          ...        ...\n",
       "995  1500100996        355\n",
       "996  1500100997        293\n",
       "997  1500100998        398\n",
       "998  1500100999        371\n",
       "999  1500101000        379\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "18a777bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 关联 merge\n",
    "# how： left right inner\n",
    "stu_score_sum = stu.merge(score_sum,left_on='id',right_on='student_id',how='left').drop('student_id',axis=1)\n",
    "# drop('student_id')   会报错，因为会默认指在索引列去找‘student_id’，\n",
    "# drop('student_id',axis=1)   axis=1 指在横坐标（即x轴去寻找列值‘student_id’）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "efa75dcf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "      <th>score_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id name  age gender clazz  score_sum\n",
       "0  1500100001  施笑槐   22      女  文科六班        406\n",
       "1  1500100002  吕金鹏   24      男  文科六班        440\n",
       "2  1500100003  单乐蕊   22      女  理科六班        359\n",
       "3  1500100004  葛德曜   24      男  理科三班        421\n",
       "4  1500100005  宣谷芹   22      女  理科五班        395"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stu_score_sum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "88389f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 按班级分组 并排序\n",
    "stu_score_sum['first_rank'] = stu_score_sum.groupby('clazz')['score_sum'].rank(method='first',ascending=False)\n",
    "# ascending=False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e641f459",
   "metadata": {},
   "outputs": [],
   "source": [
    "stu_score_sum = stu_score_sum.sort_values(['clazz','first_rank'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1d28afd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "stu_score_sum['first_rank']<=3\n",
    "# 布尔索引\n",
    "clazz_top3 = stu_score_sum[stu_score_sum['first_rank']<=3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "77b2b7ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "      <th>score_sum</th>\n",
       "      <th>first_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>1500100308</td>\n",
       "      <td>黄初夏</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科一班</td>\n",
       "      <td>628</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>1500100875</td>\n",
       "      <td>马向南</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>文科一班</td>\n",
       "      <td>595</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>1500100943</td>\n",
       "      <td>许昌黎</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>文科一班</td>\n",
       "      <td>580</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>1500100160</td>\n",
       "      <td>云冰真</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>文科三班</td>\n",
       "      <td>568</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>1500100434</td>\n",
       "      <td>黎雨珍</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科三班</td>\n",
       "      <td>550</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>1500100572</td>\n",
       "      <td>臧忆香</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科三班</td>\n",
       "      <td>550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1500100418</td>\n",
       "      <td>蓟海昌</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "      <td>文科二班</td>\n",
       "      <td>611</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>1500100823</td>\n",
       "      <td>宓新曦</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "      <td>文科二班</td>\n",
       "      <td>547</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>1500100954</td>\n",
       "      <td>咸芷天</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>文科二班</td>\n",
       "      <td>533</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>1500100930</td>\n",
       "      <td>闻运凯</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科五班</td>\n",
       "      <td>589</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>1500100949</td>\n",
       "      <td>颜沛槐</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>文科五班</td>\n",
       "      <td>564</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>1500100904</td>\n",
       "      <td>阎元蝶</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科五班</td>\n",
       "      <td>562</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1500100136</td>\n",
       "      <td>黎昆鹏</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>583</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>1500100900</td>\n",
       "      <td>查思菱</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>562</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>1500100716</td>\n",
       "      <td>丰冷霜</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>560</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>1500100873</td>\n",
       "      <td>路鸿志</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科四班</td>\n",
       "      <td>612</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>1500100258</td>\n",
       "      <td>湛昌勋</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "      <td>文科四班</td>\n",
       "      <td>604</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1500100116</td>\n",
       "      <td>文元蝶</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>文科四班</td>\n",
       "      <td>520</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1500100235</td>\n",
       "      <td>沈香巧</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>理科一班</td>\n",
       "      <td>520</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>1500100773</td>\n",
       "      <td>傅元蝶</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>理科一班</td>\n",
       "      <td>492</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>1500100925</td>\n",
       "      <td>卞乐萱</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>理科一班</td>\n",
       "      <td>486</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>1500100929</td>\n",
       "      <td>满慕易</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>理科三班</td>\n",
       "      <td>630</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>1500100184</td>\n",
       "      <td>夔寻巧</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>理科三班</td>\n",
       "      <td>580</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>1500100598</td>\n",
       "      <td>宰金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "      <td>551</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>1500100834</td>\n",
       "      <td>谷念薇</td>\n",
       "      <td>21</td>\n",
       "      <td>女</td>\n",
       "      <td>理科二班</td>\n",
       "      <td>586</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1500100104</td>\n",
       "      <td>咸冰蝶</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>理科二班</td>\n",
       "      <td>533</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>761</th>\n",
       "      <td>1500100762</td>\n",
       "      <td>聂德明</td>\n",
       "      <td>23</td>\n",
       "      <td>男</td>\n",
       "      <td>理科二班</td>\n",
       "      <td>524</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1500100080</td>\n",
       "      <td>巫景彰</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科五班</td>\n",
       "      <td>628</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>1500100547</td>\n",
       "      <td>廖向南</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "      <td>584</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>1500100839</td>\n",
       "      <td>明雁桃</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "      <td>557</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>1500100596</td>\n",
       "      <td>田晨潍</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>587</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>1500100903</td>\n",
       "      <td>於依云</td>\n",
       "      <td>24</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>549</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>1500100563</td>\n",
       "      <td>禄昆鹏</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>536</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>1500100635</td>\n",
       "      <td>蓬怀绿</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>理科四班</td>\n",
       "      <td>534</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>1500100590</td>\n",
       "      <td>逄中震</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>理科四班</td>\n",
       "      <td>530</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>1500100939</td>\n",
       "      <td>耿智杰</td>\n",
       "      <td>23</td>\n",
       "      <td>男</td>\n",
       "      <td>理科四班</td>\n",
       "      <td>530</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id name  age gender clazz  score_sum  first_rank\n",
       "307  1500100308  黄初夏   23      女  文科一班        628         1.0\n",
       "874  1500100875  马向南   21      女  文科一班        595         2.0\n",
       "942  1500100943  许昌黎   21      男  文科一班        580         3.0\n",
       "159  1500100160  云冰真   24      女  文科三班        568         1.0\n",
       "433  1500100434  黎雨珍   23      女  文科三班        550         2.0\n",
       "571  1500100572  臧忆香   23      女  文科三班        550         3.0\n",
       "417  1500100418  蓟海昌   22      男  文科二班        611         1.0\n",
       "822  1500100823  宓新曦   22      男  文科二班        547         2.0\n",
       "953  1500100954  咸芷天   21      女  文科二班        533         3.0\n",
       "929  1500100930  闻运凯   24      男  文科五班        589         1.0\n",
       "948  1500100949  颜沛槐   21      女  文科五班        564         2.0\n",
       "903  1500100904  阎元蝶   23      女  文科五班        562         3.0\n",
       "135  1500100136  黎昆鹏   22      男  文科六班        583         1.0\n",
       "899  1500100900  查思菱   24      女  文科六班        562         2.0\n",
       "715  1500100716  丰冷霜   22      女  文科六班        560         3.0\n",
       "872  1500100873  路鸿志   24      男  文科四班        612         1.0\n",
       "257  1500100258  湛昌勋   22      男  文科四班        604         2.0\n",
       "115  1500100116  文元蝶   21      女  文科四班        520         3.0\n",
       "234  1500100235  沈香巧   24      女  理科一班        520         1.0\n",
       "772  1500100773  傅元蝶   21      女  理科一班        492         2.0\n",
       "924  1500100925  卞乐萱   21      女  理科一班        486         3.0\n",
       "928  1500100929  满慕易   21      女  理科三班        630         1.0\n",
       "183  1500100184  夔寻巧   24      女  理科三班        580         2.0\n",
       "597  1500100598  宰金鹏   24      男  理科三班        551         3.0\n",
       "833  1500100834  谷念薇   21      女  理科二班        586         1.0\n",
       "103  1500100104  咸冰蝶   23      女  理科二班        533         2.0\n",
       "761  1500100762  聂德明   23      男  理科二班        524         3.0\n",
       "79   1500100080  巫景彰   21      男  理科五班        628         1.0\n",
       "546  1500100547  廖向南   22      女  理科五班        584         2.0\n",
       "838  1500100839  明雁桃   22      女  理科五班        557         3.0\n",
       "595  1500100596  田晨潍   21      男  理科六班        587         1.0\n",
       "902  1500100903  於依云   24      女  理科六班        549         2.0\n",
       "562  1500100563  禄昆鹏   22      男  理科六班        536         3.0\n",
       "634  1500100635  蓬怀绿   23      女  理科四班        534         1.0\n",
       "589  1500100590  逄中震   24      男  理科四班        530         2.0\n",
       "938  1500100939  耿智杰   23      男  理科四班        530         3.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_top3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f377beec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4a1fd3d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# windows解决中文乱码\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签\n",
    "\n",
    "# mac解决中文乱码\n",
    "# plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']\n",
    "\n",
    "plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7b79646a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))  # figsize: 指定figure的宽和高，单位为英寸\n",
    "plt.title('班级总分Top3')  #图名\n",
    "ax = sns.barplot(x=\"clazz\", y=\"score_sum\", hue=\"first_rank\", data=clazz_top3)\n",
    "plt.xlabel('班级')  # x轴名\n",
    "plt.ylabel('总分')  # y轴名\n",
    "plt.ylim(450,650)  # 分数区间\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "eb51995c",
   "metadata": {},
   "outputs": [],
   "source": [
    "clazz_gender = stu.groupby(['clazz','gender'])['id'].count().reset_index().rename(columns={'id':'cnt'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "607843d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clazz</th>\n",
       "      <th>gender</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>女</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文科一班</td>\n",
       "      <td>男</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>女</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文科三班</td>\n",
       "      <td>男</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>女</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>文科二班</td>\n",
       "      <td>男</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>文科五班</td>\n",
       "      <td>女</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>文科五班</td>\n",
       "      <td>男</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>文科六班</td>\n",
       "      <td>女</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>文科六班</td>\n",
       "      <td>男</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>文科四班</td>\n",
       "      <td>女</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>文科四班</td>\n",
       "      <td>男</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>理科一班</td>\n",
       "      <td>女</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>理科一班</td>\n",
       "      <td>男</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>理科三班</td>\n",
       "      <td>女</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>理科三班</td>\n",
       "      <td>男</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>理科二班</td>\n",
       "      <td>女</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>理科二班</td>\n",
       "      <td>男</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>理科五班</td>\n",
       "      <td>女</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>理科五班</td>\n",
       "      <td>男</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>理科六班</td>\n",
       "      <td>女</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>理科六班</td>\n",
       "      <td>男</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>理科四班</td>\n",
       "      <td>女</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>理科四班</td>\n",
       "      <td>男</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   clazz gender  cnt\n",
       "0   文科一班      女   41\n",
       "1   文科一班      男   31\n",
       "2   文科三班      女   44\n",
       "3   文科三班      男   50\n",
       "4   文科二班      女   38\n",
       "5   文科二班      男   49\n",
       "6   文科五班      女   41\n",
       "7   文科五班      男   43\n",
       "8   文科六班      女   49\n",
       "9   文科六班      男   55\n",
       "10  文科四班      女   42\n",
       "11  文科四班      男   39\n",
       "12  理科一班      女   47\n",
       "13  理科一班      男   31\n",
       "14  理科三班      女   33\n",
       "15  理科三班      男   35\n",
       "16  理科二班      女   43\n",
       "17  理科二班      男   36\n",
       "18  理科五班      女   37\n",
       "19  理科五班      男   33\n",
       "20  理科六班      女   37\n",
       "21  理科六班      男   55\n",
       "22  理科四班      女   41\n",
       "23  理科四班      男   50"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clazz_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3a78f303",
   "metadata": {},
   "outputs": [],
   "source": [
    "clazz_gender_labels = clazz_gender['clazz']+'-'+clazz_gender['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "faae28ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "# 指定扇形的宽和高\n",
    "plt.title('性别人数比例')\n",
    "plt.pie(clazz_gender['cnt'],labels=clazz_gender_labels.values,autopct='%.2f%%')\n",
    "# autopct：设置饼图内各个扇形百分比显示格式，%d%% 整数百分比，%0.1f 一位小数， %0.1f%% 一位小数百分比， %0.2f%% 两位小数百分比。\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "76cfcbc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制子图\n",
    "plt.figure(figsize=(16,12))\n",
    "\n",
    "i = 1\n",
    "for clazz in clazz_gender.clazz.unique():\n",
    "    plt.subplot(3,4,i)  #三行四列\n",
    "    plt.title('%s性别比例' %clazz)\n",
    "    clazz1 = clazz_gender[clazz_gender['clazz']==clazz]\n",
    "    plt.pie(clazz1['cnt'],labels=clazz1['gender'],autopct='%.2f%%',colors=[\"#\"+\"\".join([random.choice('0123456789ABCDEF') for i  in range(6)]) for j in range(2)])\n",
    "    i+=1\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "297cd690",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1e690280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['E', 'E', '1', '1', '0', 'C']"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# colors=[\"#d5695d\", \"#5d8ca8\"]\n",
    "list = []\n",
    "for i in range(6):\n",
    "    list.append(random.choice('0123456789ABCDEF'))\n",
    "list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "6792dae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'#71E3F9'"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列表推导式 随机生成一个RGB\n",
    "\"#\"+\"\".join([random.choice('0123456789ABCDEF') for i  in range(6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "dc764aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#397059', '#CBDC5F']"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = []\n",
    "for j in range(2):\n",
    "    list1.append(\"#\"+\"\".join([random.choice('0123456789ABCDEF') for i  in range(6)]))\n",
    "list1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "191c15ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#710154', '#A6CFB2']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 列表推导式  随机生成2个RGB\n",
    "[\"#\"+\"\".join([random.choice('0123456789ABCDEF') for i  in range(6)]) for j in range(2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "c8ed3735",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取mysql\n",
    "import pymysql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "7ea25c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = pymysql.connect(user='root',passwd='123456',host='master',port=3306,database='student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "666f1ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>clazz</th>\n",
       "      <th>last_mod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500100001</td>\n",
       "      <td>施笑槐</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>2019-03-16 14:47:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500100002</td>\n",
       "      <td>吕金鹏</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科六班</td>\n",
       "      <td>2019-03-16 14:47:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1500100003</td>\n",
       "      <td>单乐蕊</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>2019-03-16 14:47:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500100004</td>\n",
       "      <td>葛德曜</td>\n",
       "      <td>25</td>\n",
       "      <td>男</td>\n",
       "      <td>理科三班</td>\n",
       "      <td>2019-03-16 14:49:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1500100005</td>\n",
       "      <td>宣谷芹</td>\n",
       "      <td>22</td>\n",
       "      <td>女</td>\n",
       "      <td>理科五班</td>\n",
       "      <td>2019-03-16 14:47:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1500100996</td>\n",
       "      <td>厉运凡</td>\n",
       "      <td>24</td>\n",
       "      <td>男</td>\n",
       "      <td>文科三班</td>\n",
       "      <td>2019-03-16 14:47:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1500100997</td>\n",
       "      <td>陶敬曦</td>\n",
       "      <td>21</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>2019-03-16 14:47:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1500100998</td>\n",
       "      <td>容昆宇</td>\n",
       "      <td>22</td>\n",
       "      <td>男</td>\n",
       "      <td>理科四班</td>\n",
       "      <td>2019-03-16 14:47:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1500100999</td>\n",
       "      <td>钟绮晴</td>\n",
       "      <td>23</td>\n",
       "      <td>女</td>\n",
       "      <td>文科五班</td>\n",
       "      <td>2019-03-16 14:47:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1500101000</td>\n",
       "      <td>符瑞渊</td>\n",
       "      <td>26</td>\n",
       "      <td>男</td>\n",
       "      <td>理科六班</td>\n",
       "      <td>2019-03-16 15:06:25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id name  age gender clazz            last_mod\n",
       "0    1500100001  施笑槐   22      女  文科六班 2019-03-16 14:47:59\n",
       "1    1500100002  吕金鹏   24      男  文科六班 2019-03-16 14:47:59\n",
       "2    1500100003  单乐蕊   22      女  理科六班 2019-03-16 14:47:59\n",
       "3    1500100004  葛德曜   25      男  理科三班 2019-03-16 14:49:01\n",
       "4    1500100005  宣谷芹   22      女  理科五班 2019-03-16 14:47:59\n",
       "..          ...  ...  ...    ...   ...                 ...\n",
       "995  1500100996  厉运凡   24      男  文科三班 2019-03-16 14:47:59\n",
       "996  1500100997  陶敬曦   21      男  理科六班 2019-03-16 14:47:59\n",
       "997  1500100998  容昆宇   22      男  理科四班 2019-03-16 14:47:59\n",
       "998  1500100999  钟绮晴   23      女  文科五班 2019-03-16 14:47:59\n",
       "999  1500101000  符瑞渊   26      男  理科六班 2019-03-16 15:06:25\n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql(\"select * from student\",con=conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "cdf41a0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出学生成绩表\n",
    "stu_score_sum.to_csv(\"./data/stu_score_sum.csv\",index=None,header=None,encoding=\"utf8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "665bfffe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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